docstr: Class that holds a generation configuration for [`BarkCoarseModel`].
  
  This configuration inherit from [`GenerationConfig`] and can be used to control the model generation. Read the
  documentation from [`GenerationConfig`] for more information.
  
  Args:
      renormalize_logits (`bool`, *optional*, defaults to `True`):
          Whether to renormalize the logits after applying all the logits processors or warpers (including the
          custom ones). It's highly recommended to set this flag to `True` as the search algorithms suppose the
          score logits are normalized but some logit processors or warpers break the normalization.
      output_scores (`bool`, *optional*, defaults to `False`):
          Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
      return_dict_in_generate (`bool`, *optional*, defaults to `False`):
          Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
      output_hidden_states (`bool`, *optional*, defaults to `False`):
          Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
          for more details.
      output_attentions (`bool`, *optional*, defaults to `False`):
          Whether or not to return the attentions tensors of all attention layers. See `attentions` under
          returned tensors for more details.
      temperature (`float`, *optional*, defaults to 1.0):
          The value used to modulate the next token probabilities.
      do_sample (`bool`, *optional*, defaults to `False`):
          Whether or not to use sampling ; use greedy decoding otherwise.
      coarse_semantic_pad_token (`int`, *optional*, defaults to 12_048):
          Coarse semantic pad token.
      coarse_rate_hz (`int`, *optional*, defaults to 75):
          Coarse rate in Hertz.
      n_coarse_codebooks (`int`, *optional*, defaults to 2):
          Number of coarse codebooks.
      coarse_infer_token (`int`, *optional*, defaults to 12_050):
          Coarse infer token.
      max_coarse_input_length (`int`, *optional*, defaults to 256):
          Max length of input coarse vector.
      max_coarse_history (`int`, *optional*, defaults to 630):
          Max length of the output of the coarse acoustics model used in the fine generation step.
      sliding_window_len (`int`, *optional*, defaults to 60):
          The coarse generation step uses a sliding window to generate raw audio.
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code: def __init__(
      self,
      renormalize_logits=True,
      output_scores=False,
      return_dict_in_generate=False,
      output_hidden_states=False,
      output_attentions=False,
      temperature=1.0,
      do_sample=False,
      coarse_semantic_pad_token=12_048,
      coarse_rate_hz=75,
      n_coarse_codebooks=2,
      coarse_infer_token=12_050,
      max_coarse_input_length=256,
      max_coarse_history: int = 630,
      sliding_window_len: int = 60,
      **kwargs,
  ):
      """Class that holds a generation configuration for [`BarkCoarseModel`].
  
      This configuration inherit from [`GenerationConfig`] and can be used to control the model generation. Read the
      documentation from [`GenerationConfig`] for more information.
  
      Args:
          renormalize_logits (`bool`, *optional*, defaults to `True`):
              Whether to renormalize the logits after applying all the logits processors or warpers (including the
              custom ones). It's highly recommended to set this flag to `True` as the search algorithms suppose the
              score logits are normalized but some logit processors or warpers break the normalization.
          output_scores (`bool`, *optional*, defaults to `False`):
              Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
          return_dict_in_generate (`bool`, *optional*, defaults to `False`):
              Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
          output_hidden_states (`bool`, *optional*, defaults to `False`):
              Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
              for more details.
          output_attentions (`bool`, *optional*, defaults to `False`):
              Whether or not to return the attentions tensors of all attention layers. See `attentions` under
              returned tensors for more details.
          temperature (`float`, *optional*, defaults to 1.0):
              The value used to modulate the next token probabilities.
          do_sample (`bool`, *optional*, defaults to `False`):
              Whether or not to use sampling ; use greedy decoding otherwise.
          coarse_semantic_pad_token (`int`, *optional*, defaults to 12_048):
              Coarse semantic pad token.
          coarse_rate_hz (`int`, *optional*, defaults to 75):
              Coarse rate in Hertz.
          n_coarse_codebooks (`int`, *optional*, defaults to 2):
              Number of coarse codebooks.
          coarse_infer_token (`int`, *optional*, defaults to 12_050):
              Coarse infer token.
          max_coarse_input_length (`int`, *optional*, defaults to 256):
              Max length of input coarse vector.
          max_coarse_history (`int`, *optional*, defaults to 630):
              Max length of the output of the coarse acoustics model used in the fine generation step.
          sliding_window_len (`int`, *optional*, defaults to 60):
              The coarse generation step uses a sliding window to generate raw audio.
      """
      super().__init__(
          temperature=temperature,
          do_sample=do_sample,
          renormalize_logits=renormalize_logits,
          output_scores=output_scores,
          return_dict_in_generate=return_dict_in_generate,
          output_hidden_states=output_hidden_states,
          output_attentions=output_attentions,
          **kwargs,
      )
  
      self.coarse_semantic_pad_token = coarse_semantic_pad_token
      self.coarse_rate_hz = coarse_rate_hz
      self.n_coarse_codebooks = n_coarse_codebooks
      self.coarse_infer_token = coarse_infer_token
      self.max_coarse_input_length = max_coarse_input_length
      self.max_coarse_history = max_coarse_history
      self.sliding_window_len = sliding_window_len
  
file_path: transformers/src/transformers/models/bark/generation_configuration_bark.py
imports: import copy
  from typing import Dict
  from ...generation.configuration_utils import GenerationConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new BarkCoarseGenerationConfig object from inherited base classes: GenerationConfig with example: obj = BarkCoarseGenerationConfig(renormalize_logits, output_scores, return_dict_in_generate, output_hidden_states, output_attentions, temperature, do_sample, coarse_semantic_pad_token, coarse_rate_hz, n_coarse_codebooks, coarse_infer_token, max_coarse_input_length, max_coarse_history, sliding_window_len)